We also provide direct comparison to the current state-of-the-art method, HSNet [1]}, in Table REF . We observe that memory consumption gap is quite large, which is an apparent limitation to the proposed method. Run-time gap is quite trivial, enabling real-time inference. Note that standard transformer can not be used due to OOM. In light of this, although we managed to propose a method to aggregate a raw correlation map without arbitrarily changing its resolutions prior to feeding into networks, we observe relatively large memory consumption gap to HSNet [1]}, which suggests a promising direction for future work, i.e., even more efficient method while enabling to process at higher spatial resolutions.
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